{"id":102454,"date":"2021-12-03T15:36:38","date_gmt":"2021-12-03T13:36:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij"},"modified":"2021-12-03T15:36:38","modified_gmt":"2021-12-03T13:36:42","slug":"hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/novosti-interneta\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","title":{"rendered":"HyperStyle — adattamento del sistema di apprendimento automatico StyleGAN per la modifica delle immagini","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un gruppo di ricercatori dell'Universit\u00e0 di Tel Aviv ha presentato HyperStyle, una versione invertita del sistema di machine learning StyleGAN2 sviluppato da NVIDIA, riprogettato per ricreare parti mancanti durante l'editing di immagini reali. Il codice \u00e8 scritto in Python utilizzando il framework PyTorch ed \u00e8 distribuito con licenza MIT. <\/p>\n

Se StyleGAN consente di sintetizzare nuovi volti umani dall\u2019aspetto realistico, impostando parametri come et\u00e0, genere, lunghezza dei capelli, espressione del sorriso, forma del naso, colore della pelle, occhiali e angolazione della foto, HyperStyle offre invece la possibilit\u00e0 di modificare parametri simili su fotografie esistenti, senza alterare le caratteristiche distintive e mantenendo il riconoscimento del volto originale. Ad esempio, tramite HyperStyle \u00e8 possibile simulare il cambiamento di et\u00e0 di una persona in una foto, cambiare acconciatura, aggiungere occhiali, barba o baffi, trasformare l\u2019immagine in quella di un personaggio di cartone animato o di un dipinto, e rendere l\u2019espressione facciale triste o felice. Inoltre, il sistema pu\u00f2 essere addestrato non solo per modificare i volti delle persone, ma anche per oggetti di qualsiasi tipo, come ad esempio la modifica delle immagini di automobili.

\"HyperStyle<\/center> <\/p>\n

Il metodo proposto si concentra sulla risoluzione del problema della ricostruzione delle parti mancanti di un'immagine durante la modifica. Nei metodi precedentemente suggeriti, il compromesso tra ricostruzione e modificabilit\u00e0 veniva affrontato tramite un'ottimizzazione fine del generatore di immagini per sostituire le parti dell'immagine target durante la ricreazione delle aree modificabili originariamente assenti. Un inconveniente di tali approcci \u00e8 la necessit\u00e0 di un lungo addestramento mirato della rete neurale per ogni immagine. <\/p>\n

Il metodo basato sull'algoritmo StyleGAN consente di utilizzare un modello standard, pre-addestrato su collezioni di immagini generali, per generare elementi propri dell'immagine originale con un livello di accuratezza paragonabile agli algoritmi che richiedono un addestramento individuale del modello per ogni immagine. Tra i vantaggi del nuovo metodo si segnala anche la possibilit\u00e0 di modificare le immagini con prestazioni simili a quelle in tempo reale.

\"HyperStyle<\/center> <\/p>\n

Modelli pre-addestrati sono disponibili per volti umani, macchine e animali, basati su collezioni come Flickr-Faces-HQ (FFHQ, 70.000 immagini PNG di volti di alta qualit\u00e0), Stanford Cars (16.000 immagini di automobili) e AFHQ (foto di animali). \u00c8 inoltre fornito un toolkit per addestrare i propri modelli, insieme a modelli pre-addestrati dei codificatori e generatori standard utilizzabili. Ad esempio, sono disponibili generatori per creare immagini in stile Toonify, personaggi Pixar, generazione di schizzi e anche per la stilizzazione di principesse dei film Disney.

\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0413\u0440\u0443\u043f\u043f\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0438\u0437 \u0422\u0435\u043b\u044c-\u0410\u0432\u0438\u0432\u0441\u043a\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 HyperStyle, \u0438\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u043e\u0439 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 NVIDIA \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f StyleGAN2, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u0435\u0440\u0435\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0434\u043b\u044f \u0432\u043e\u0441\u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u044e\u0449\u0438\u0445 \u0447\u0430\u0441\u0442\u0435\u0439 \u043f\u0440\u0438 \u0440\u0435\u0434\u0430\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 PyTorch \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 MIT. \u0415\u0441\u043b\u0438 StyleGAN \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0441\u0438\u043d\u0442\u0435\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0440\u0435\u0430\u043b\u0438\u0441\u0442\u0438\u0447\u043d\u043e \u0432\u044b\u0433\u043b\u044f\u0434\u044f\u0449\u0438\u0435 \u043d\u043e\u0432\u044b\u0435 \u043b\u0438\u0446\u0430 \u043b\u044e\u0434\u0435\u0439, \u0437\u0430\u0434\u0430\u0432\u0430\u044f \u0442\u0430\u043a\u0438\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b, \u043a\u0430\u043a \u0432\u043e\u0437\u0440\u0430\u0441\u0442, \u043f\u043e\u043b, […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":102455,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-102454","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47HyperStyle \u2014 adattamento del sistema di machine learning StyleGAN per la modifica delle immagini | ProHoster","description":"Un gruppo di ricercatori dell'Universit\u00e0 di Tel Aviv ha presentato HyperStyle, una versione invertita del sistema di apprendimento automatico StyleGAN2 sviluppato da NVIDIA, riadattato per ricreare le parti mancanti durante la modifica di immagini reali. Il codice \u00e8 scritto in Python utilizzando il framework PyTorch ed \u00e8 distribuito sotto la licenza MIT. Mentre StyleGAN consente di sintetizzare nuovi volti realistici, impostando parametri come et\u00e0, sesso,","canonical_url":"https:\/\/prohoster.info\/it\/blog\/novosti-interneta\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"it_IT","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47HyperStyle \u2014 \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0438\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f 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